Search results for "Models"

Uncertainty evaluation of design rainfall for urban flood risk analysis

2011

A reliable and long dataset describing urban flood locations, volumes and depths would be an ideal prerequisite for assessing flood frequency distributions. However, data are often piecemeal and long-term hydraulic modelling is often adopted to estimate floods from historical rainfall series. Long-term modelling approaches are time- and resource-consuming, and synthetically designed rainfalls are often used to estimate flood frequencies. The present paper aimsto assess the uncertainty of such an approach and for suggesting improvements in thedefinition of synthetic rainfall data for flooding frequency analysis. According to this aim, a multivariate statistical analysis based on a copulameth…

Serological prevalence of toxoplasmosis in pregnant women in Luanda (Angola): Geospatial distribution and its association with socio-demographic and …

2020

We report a study on toxoplasmosis in pregnant women in Luanda, Angola, determining the seroprevalence, geospatial distribution and its association with socio-economic features, dietary habits and hygiene and health conditions. Anti-Toxoplasma gondii IgG and IgM were quantified in serum samples of women attended at the Lucrecia Paim Maternity Hospital between May 2016 and August 2017. The IgG avidity test and qPCR assay were used for dating the primary infection. Data were collected by questionnaire after written consent, and spatial distribution was assessed through a Kernel Density Function. The potential risk factors associated with Toxoplasma infection were evaluated using bivariate and…

Assessment of the statistical significance of classifications in infrared spectroscopy based diagnostic models.

2014

Fourier transform infrared (IR) spectroscopy in combination with multivariate data analysis is a versatile tool that can be applied to disease diagnosis. However, a rigorous validation of the obtained models is necessary in order to obtain robust results. This work evaluates the advantages of the use of permutation testing for determining the statistical significance of the misclassification errors obtained from IR based diagnostic models through cross validation (CV). The model performance, estimated by CV, is compared to a distribution of CV-performance values obtained using randomly permuted class labels. The distribution of ‘random CV-values’ is considered as a null distribution and use…

T-pattern analysis for the study of temporal structure of animal and human behavior : a comprehensive review

2014

A basic tenet in the realm of modern behavioral sciences is that behavior consists of patterns in time. For this reason, investigations of behavior deal with sequences that are not easily perceivable by the unaided observer. This problem calls for improved means of detection, data handling and analysis. This review focuses on the analysis of the temporal structure of behavior carried out by means of a multivariate approach known as T-pattern analysis. Using this technique, recurring sequences of behavioral events, usually hard to detect, can be unveiled and carefully described. T-pattern analysis has been successfully applied in the study of various aspects of human or animal behavior such …

New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in …

2008

A new cut-off criterion has been proposed for the selection of uninformative variables prior to chemometric partial least squares (PLS) modelling. After variable elimination, PLS regressions were made and assessed comparing the results with those obtained by PLS models based on the full spectral range. To assess the prediction capabilities, uninformative variable elimination (UVE)-PLS and PLS were applied to diffuse reflectance near-infrared spectra of heroin samples. The application of the proposed new cut-off criterion, based on the t-Students distribution, provided similar predictive capabilities of the PLS models than those obtained using the original criteria based on quantile value. H…

Multivariate autoregressive model with instantaneous effects to improve brain connectivity estimation

2009

Embedding Quantum into Classical: Contextualization vs Conditionalization

2014

We compare two approaches to embedding joint distributions of random variables recorded under different conditions (such as spins of entangled particles for different settings) into the framework of classical, Kolmogorovian probability theory. In the contextualization approach each random variable is "automatically" labeled by all conditions under which it is recorded, and the random variables across a set of mutually exclusive conditions are probabilistically coupled (imposed a joint distribution upon). Analysis of all possible probabilistic couplings for a given set of random variables allows one to characterize various relations between their separate distributions (such as Bell-type ine…

Structural invariants for the prediction of relative toxicities of polychloro dibenzo-p-dioxins and dibenzofurans

2004

Multivariate models are reported that can predict the relative toxicity of compounds with severe environmental impact, namely polychloro dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs). Multiple linear regression analysis (MLR) and partial least square projections of latent variables (PLS) show the usefulness of graph-theoretical descriptors, mainly topological charge indices (TCIs), in these series. The general trends of the group are correctly reproduced and better results are presented than have previously been published. In general, the more toxic compounds exhibit more symmetric molecular structures.

Assessing Frequency Domain Causality in Cardiovascular Time Series with Instantaneous Interactions

2009

Summary Background: The partial directed coherence (PDC) is commonly used to assess in the frequency domain the existence of causal relations between two time series measured in conjunction with a set of other time series. Although the multivariate autoregressive (MVAR) model traditionally used for PDC computation accounts only for lagged effects, instantaneous effects cannot be neglected in the analysis of cardiovascular time series. Objectives: We propose the utilization of an extended MVAR model for PDC computation, in order to improve the evaluation of frequency domain causality in the presence of zero-lag correlations among multivariate time series. Methods: A procedure for the identif…

Information Dynamics Analysis: A new approach based on Sparse Identification of Linear Parametric Models*

2020

The framework of information dynamics allows to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of a complex network. The information transfer from one process to another can be quantified through Transfer Entropy, and under the assumption of joint Gaussian variables it is strictly related to the concept of Granger Causality (GC). According to the most recent developments in the field, the computation of GC entails representing the processes through a Vector Autoregressive (VAR) model and a state space (SS) model typically identified by means of the Ordinary Least Squares (OLS). In this work, we propose a new identification …